Explore every episode of the podcast Impact AI
| Title | Pub. Date | Duration | |
|---|---|---|---|
| Impact AI Update: Summer Break & Webinar Resources | 16 juin 2025 | 00:02:23 | |
I will be taking a brief hiatus for the next three months. I’m going to be using this time to step back, reflect, and rework the format of the show to bring you even more valuable insights and engaging conversations. I’m looking forward to returning in the fall with fresh episodes, new guests, and even deeper dives into the challenges and opportunities shaping mission-driven machine learning-powered companies. In the meantime, I'm thrilled to share another way you can continue to learn and engage with the world of AI through Pixel Scientia Labs. While the podcast is on pause, I invite you to explore our Webinar Initiative at pixelscientia.com/webinars. Links: | |||
| Advancing Breast Cancer Screening with Nico Karssemeijer from ScreenPoint Medical | 02 juin 2025 | 00:21:04 | |
What role can artificial intelligence play in detecting breast cancer earlier, when it's most treatable? In this episode of Impact AI, we hear from Nico Karssemeijer, Chief Science Officer of ScreenPoint Medical, about how his team is using AI to transform breast cancer screening. Drawing on more than four decades of experience in medical imaging, Nico shares how ScreenPoint’s AI tools assist radiologists by analyzing mammograms, highlighting suspicious areas, and even learning from years of patient data. The conversation explores what it takes to build trustworthy medical AI, overcome challenges with data diversity and device bias, and the importance of clinical validation. To find out how AI is being integrated into real-world healthcare to improve outcomes (and what goes into building a successful AI-powered medical company), tune in today! Key Points:
Quotes: “It’s amazing how much more information you can get out of the mammograms [using AI]. That surprises me all the time.” — Nico Karssemeijer “You can't just say, ‘This mammogram is abnormal,’ because then [the radiologists] are puzzled. – The algorithm is getting so good that it identifies areas the radiologists would probably not see by themselves. – You have to – mark the area in the exam where a lesion is found.” — Nico Karssemeijer “It's incredibly important to have enough domain expertise when you start a company, because it's easy to fail because you don't understand well enough what the customer wants [or] where the field is going.” — Nico Karssemeijer Links: Nico Karssemeijer on Google Scholar
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Radiology Tools for Precision Medicine with Ángel Alberich-Bayarri from Quibim | 19 mai 2025 | 00:32:58 | |
How can we harness medical imaging and artificial intelligence to shift healthcare from reactive to predictive? In this episode, I sit down with Ángel Alberich-Bayarri to discuss how artificial intelligence is revolutionizing radiology and precision medicine. Ángel is the CEO of Quibim, a company recognized globally for its AI-powered tools that turn radiological scans into predictive biomarkers, enabling more precise diagnoses and personalized treatments. In our conversation, we hear how his early work in radiology and engineering led to the founding of Quibim and how the company’s AI-based technology transforms medical images into predictive biomarkers. We unpack the challenges of data heterogeneity, how Quibim tackles image harmonization using self-supervised learning, and why accounting for regulations is critical when building healthcare AI products. Ángel also shares his perspective on the value of model explainability, the concept of digital twins, and the future of preventative imaging. Join us to discover how AI is disrupting clinical decision-making and preventive healthcare with Ángel Alberich-Bayarri. Key Points:
Quotes: “We would like AI to be able to mine all this hidden information we have right now in the images. Our vision is long-term, being able to understand what is happening until this point within the human body.” — Ángel Alberich-Bayarri “What [Quibim is] investing in is the next frontier that not only detects and diagnoses disease, but also predicts or prognoses what is going to happen.” — Ángel Alberich-Bayarri “Human behavior has a lot of nuances that need to be appreciated when AI is adopted.” — Ángel Alberich-Bayarri “The bolder the claims you make, it’s the higher level of evidence you need to achieve.” — Ángel Alberich-Bayarri “Taking care of health before we have symptoms, it’s just going to be a growing business, and therefore, a lot of AI tools will be needed to understand our inner us.” — Ángel Alberich-Bayarri Links: Ángel Alberich-Bayarri on LinkedIn
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Simulating Clinical Trials with Orr Inbarr from Quant Health | 05 mai 2025 | 00:21:29 | |
Drug development is notoriously time-consuming and expensive, but what if we could simulate clinical trials before they even begin? Orr Inbar, Co-Founder and CEO of QuantHealth, joins me to explore how his team is doing just that. By simulating trials with AI-native models, QuantHealth helps pharmaceutical companies make better decisions about how to design trials and test drugs. Orr shares how QuantHealth uses real-world patient data and detailed drug biology to build deep-learning models capable of forecasting patient responses to new therapies. He breaks down their biggest challenges, like the complexities of messy healthcare data, hidden biases, and the importance of domain knowledge when building AI tools for regulated environments. He also shares a key lesson for any AI startup: focus on solving real problems, not just building clever models. Tune in for a fascinating look at how AI is reshaping drug development and what the future of clinical trials could look like! Key Points:
“There is a constant desire in drug development and pharmaceutical research to get your hands on more data. This makes sense since it's a very data-driven industry. But at the same time, there was a mismatch there, because there's actually quite a lot of data already out there.” — Orr Inbar “How do we bridge the gap between the data that we already have and the insights that we need to generate to answer those questions?” — Orr Inbar “If you take a step back and look at how drugs are being developed today and with an emphasis on clinical trials, we're essentially doing the same things that we were doing 50 years ago.” — Orr Inbar “Even in a world of GenAI, you can't just snap your fingers and get the solution. It requires a lot of work to structure and harmonize the data.” — Orr Inbar “Every trial that we simulate, we first go through a data enrichment process where we look for the latest information in terms of research publications, recently completed trials that are relevant to our drug of interest, and incorporate that data into our data sets.” — Orr Inbar Links: Orr Inbar on LinkedIn LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Early Wildfire Detection with Shahab Bahrami from SenseNet | 21 avr. 2025 | 00:23:13 | |
The recent destruction of the Pacific Palisades in Los Angeles was a brutal reminder of why we need robust early wildfire detection systems. Joining me today is Shahab Bahrami, the co-founder and CTO at SenseNet – a company that provides advanced AI-powered cameras and sensors to protect communities and valuable assets against wildfires. Shahab is passionate about using interdisciplinary research to bridge the gap between machine learning and optimization, and he begins today’s conversation by detailing his professional background and how it led him to co-found SenseNet. Then, we unpack SenseNet and how its technology works, how it gathers data for its AI models, the challenges of relying on images and other sensor data to train machine learning models, and how SenseNet uses multiple sources to detect or define any one problem. To end, we learn why and how SenseNet uses various AI models in a single sensor, how it measures the overall impact of its tech, where the company plans to be in the next five years, and Shahab’s valuable advice for other leaders of AI-powered startups. Key Points:
Quotes: “We have one of the most comprehensive wildfire detection solutions in the world, and it is proven by multiple, real-world projects.” — Shahab Bahrami “Having separate AI models is the solution that we are now implementing.” — Shahab Bahrami “For the sensor’s AI – because it is a semi-supervised AI, it automatically adapts itself to local conditions. It learns gradually what is normal and what is abnormal, and it is a continuous learning. It won’t stop.” — Shahab Bahrami “AI changes fast. Every day we have a new AI engine, we have a new model, and leaders, I believe, need to stay updated and make sure their teams have the support and also the resources to keep innovating.” — Shahab Bahrami Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Foundation Model Series: Empowering Drug Discovery with Rick Schneider from Helical | 07 avr. 2025 | 00:22:10 | |
AI is transforming drug discovery by making biological data more accessible and actionable, bridging the gap between complex sequencing data and real-world therapeutic breakthroughs. As Rick Schneider puts it, it's all about leveraging powerful models to “build use cases that matter and bring value.” In this episode of Impact AI, we hear from the CEO and Co-founder of Helical to find out how bio-foundation models are transforming pharmaceutical research. Rick shares how Helical’s AI platform enables drug discovery by leveraging biological sequencing data without requiring companies to build their own models from scratch. He also reveals the challenges of working with high-dimensional biological data, the power of model specialization for specific therapeutic areas, and the growing role of open-source AI in healthcare innovation. Whether you're in biotech, AI, or simply curious about the future of medicine, this episode offers invaluable insights into how AI is shaping the next generation of drug discovery. Tune in today! Key Points:
Quotes: “The question is, how do I leverage [powerful biological foundation models] and – build use cases that matter and bring value? Helical is building a therapeutic area, an agnostic AI platform that is empowering single-cell RNA and DNA bio foundation models for drug discovery.” — Rick Schneider “In bio, you can still innovate on the architecture side and not simply [with] the scale of the models. It's not simply by throwing more compute at the models that you get to the very best outcomes.” — Rick Schneider “Be okay with being different in your approach and accept [that you will] be contrarian to certain things.” — Rick Schneider Links: Helix-mRNA: A Hybrid Foundation Model For Full Sequence mRNA Therapeutics
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Streamlining Radiology with Junaid Kalia from NeuroCareAI | 24 mars 2025 | 00:22:29 | |
AI tools for healthcare are becoming more prevalent than ever before, and today, we explore how this could help usher in a future of democratized healthcare for all. I am joined by the neurocritical stroke and epilepsy specialist Junaid Kalia, MD, founder of NeuroCareAI – an innovative enterprise utilizing artificial intelligence solutions to enhance health outcomes and efficiency. Junaid begins with his professional background and what led him to found NeuroCareAI before explaining what his company does and the products and services it offers. Then, we unpack the primary data sets that inform NeuroCareAI’s work, how to overcome the challenges of combining varied data types, the ethical responsibilities of AI, and how to ensure generalizability is upheld over long periods. To end, we learn why it’s essential to distinguish explainability from reason, how to mitigate the effects of bias on radiology data, how the regulatory process stunts the development of machine learning solutions, and Junaid’s vision of the future of NeuroCareAI. Key Points:
Quotes: “Coming from a very low resource country like Pakistan, I wanted to start a project in which AI can help democratize in countries with low resource settings.” — Junaid Kalia “Our mission is if you save a life, it is as if you save the life of all mankind.” — Junaid Kalia “When you are deploying artificial intelligence, you need to make sure that it's deployed ethically. [For] some of these things, we do expect our partner sites – [to] have a real quality assurance system in place before they can deploy my artificial intelligence, because I just want to be ethical.” — Junaid Kalia “We need to differentiate [and] distinguish between reasoning and explainability. In the vision world, I believe that explainability is nice to have. In the large language models space, reasoning, in my opinion, is a must-have.” — Junaid Kalia Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Foundation Model Series: Advancing Precision Medicine in Radiology with Paul Hérent from Raidium | 03 mars 2025 | 00:22:36 | |
Radiologists face a growing demand for imaging analysis, yet existing AI tools remain fragmented, each solving only a small part of the workflow. Today, we continue our series on domain-specific foundation models with Paul Hérent, Co-Founder and CEO of Raidium. He joins us to discuss how foundation models could revolutionize radiology by providing a single AI-powered solution for multiple imaging modalities. Paul shares his journey from radiologist to AI entrepreneur, explaining how his background in cognitive science and medical imaging led him to co-found Raidium. He breaks down the challenges of building a foundation model for radiology, from handling massive datasets to addressing bias and regulatory hurdles, and their approach at Raidium. We also explore Raidium’s vision for the future: its plans to refine multimodal AI, expand its applications beyond radiology, and commercialize its technology to improve patient care worldwide. Tune in to learn how foundation models could shape the future of radiology, enhance patient care, and expand global access to medical imaging! Key Points:
Quotes: “In practice, there is still little AI adoption because every solution solves only a tiny part of what radiologist do. [For radiologists] it's a wider job. We want, as a radiologist, to have one tool to rule all modalities.” — Paul Hérent “Data is key. If you have good data, not only to build a data set, but proprietary data, challenging data, rare data in a specific domain. It's very valuable because the architecture is not particularly innovative.” — Paul Hérent “Build a team with people you trust. Entrepreneurship is not trivial. Be complementary.” — Paul Hérent “The dream of Raidium is to build something that has a huge impact on a patient's life.” — Paul Hérent “If we go beyond the rich countries, many, many people have no access to radiology. Two-thirds of countries don’t have access to radiologists. It's a big need. If we can contribute with our approach to more accessible health, we will be very happy.” — Paul Hérent Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Foundation Model Series: Advancing Endoscopy with Matt Schwartz from Virgo | 24 févr. 2025 | 00:21:04 | |
What if a routine endoscopy could do more than just detect disease by actually predicting treatment outcomes and revolutionizing precision medicine? In this episode of Impact AI, Matt Schwartz, CEO and Co-Founder of endoscopy video management and AI analysis platform Virgo, discusses how AI and machine learning are transforming endoscopy. Tuning in, you’ll learn how Virgo’s foundation model, EndoDINO, trained on the largest endoscopic video dataset in the world, is unlocking new possibilities in gastroenterology. Matt also shares how automated video capture, AI-powered diagnostics, and predictive analytics are reshaping patient care, with a particular focus on improving treatment for inflammatory bowel disease (IBD). Join us to discover how domain-specific foundation models are redefining healthcare and what this means for the future of precision medicine! Key Points:
Quotes: “There's a massive amount of endoscopic video data being generated across a wide range of endoscopic procedures, and nobody was capturing that data – [Virgo] realized early on that endoscopy data could hold the key to unlocking all sorts of opportunities in precision medicine.” — Matt Schwartz “With the foundation model paradigm, you can compress a lot of heavy compute needs into a single model and then build different applications on top of the foundation. This is going to have a positive impact on the clinical deployment of foundation models.” — Matt Schwartz “Our foundation model can turn something like a routine colonoscopy into a precision medicine screening tool for IBD patients.” — Matt Schwartz “There are a lot of untapped data resources in healthcare. If a founder can build a first product that is the data capture engine, it will set them up for a ton of future success when it comes to AI development.” — Matt Schwartz Links: Introducing EndoDINO: A Breakthrough in Endoscopic AI
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Foundation Model Series: Transforming Biology with Zelda Mariet from Bioptimus | 17 févr. 2025 | 00:21:26 | |
Zelda Mariet, Co-Founder and Principal Research Scientist at Bioptimus, joins me to continue our series of conversations on the vast possibilities and diverse applications of foundation models. Today’s discussion focuses on how foundation models are transforming biology. Zelda shares insights into Bioptimus’ work and why it’s so critical in this field. She breaks down the three core components involved in building these models and explains what sets their histopathology model apart from the many others being published today. They also explore the methodology for properly benchmarking the quality and performance of foundation models, Bioptimus’ strategy for commercializing its technology, and much more. To learn more about Bioptimus, their plans beyond pathology, and the impact they hope to make in the next three to five years, tune in now. Key Points:
Quotes: “Working on biological data became a little bit of a fascination of mine because I was so instinctively annoyed at how hard it was to do.” — Zelda Mariet “Bioptimus is building foundation models for biology. Foundation models are essentially machine learning models that take an extremely long time to train [and] are trained over an incredible amount of data.” — Zelda Mariet “There are two things that are well-known about foundation models, they’re hungry in terms of data and they’re hungry in terms of compute.” — Zelda Mariet “On the philosophical side, science is something that progresses as a community, and as much as we have, what I would say is a frankly amazing team at Bioptimus, we don’t have a monopoly on people who understand the problems we’re trying to solve. And having our model be accessible is one way to gain access into the broader community to get insight and to help people who want to use our models, get insight into maybe where we’re not doing as well that we need to improve.” — Zelda Mariet Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Foundation Model Series: Democratizing Time Series Data Analysis with Max Mergenthaler Canseco from Nixtla | 10 févr. 2025 | 00:27:11 | |
What if the hidden patterns of time series data could be unlocked to predict the future with remarkable accuracy? In this episode of Impact AI, I sit down with Max Mergenthaler Canseco to discuss democratizing time series data analysis through the development of foundation models. Max is the CEO and co-founder of Nixtla, a company specializing in time series research and deployment, aiming to democratize access to advanced predictive insights across various industries. In our conversation, we explore the significance of time series data in real-world applications, the evolution of time series forecasting, and the shift away from traditional econometric models to the development of TimeGPT. Learn about the challenges faced in building foundation models for time series and a time series model’s practical applications across industries. Discover the future of time series models, the integration of multimodal data, scaling challenges, and the potential for greater adoption in both small businesses and large enterprises. Max also shares Nixtla’s vision for becoming the go-to solution for time series analysis and offers advice to leaders of AI-powered startups. Key Points:
Quotes: “Time series are in one aspect, the DNA of the world.” — Max Mergenthaler Canseco “Time is an essential component to understand a change of course, but also to understand our reality. So, time series is maybe a somewhat technical term for a very familiar aspect of our reality.” — Max Mergenthaler Canseco “Given that we are all training on massive amounts of data and some of us are not disclosing which datasets we’re using, it’s always a problem for academics to try to benchmark foundation models because there might be leakage.” — Max Mergenthaler Canseco “That’s an interesting aspect of foundation models in time series, that benchmarking is not as straightforward as one might think.” — Max Mergenthaler Canseco “I think right now in our field probably benchmarks are not necessarily indicative of how well a model is going to perform in real-world data.” — Max Mergenthaler Canseco “I think that we’re also going to see some of those intuitions that come from the LLM field translated into the time series field soon.” — Max Mergenthaler Canseco Links: Max Mergenthaler Canseco on LinkedIn
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Foundation Model Series: Harnessing Multimodal Data to Advance Immunotherapies with Ron Alfa from Noetik | 03 févr. 2025 | 00:33:53 | |
In this episode, I'm joined by Ron Alfa, Co-Founder and CEO of Noetik, to discuss the groundbreaking role of foundation models in advancing cancer immunotherapy. Together, we explore why these models are essential to his work, what it takes to build a model that understands biology, and how Noetik is creating and sourcing their datasets. Ron also shares insights on scaling and training these models, the challenges his team has faced, and how effective analysis helps determine a model’s quality. To learn more about Noetik’s innovative achievements, Ron’s advice for leaders in AI-powered startups, and much more, be sure to tune in! Key Points:
Quotes: “Our thesis for Noetik is that one of the biggest problems we can impact if we want to make and bring new drugs to patients is predicting clinical success; so-called translation — that's where we focus Noetik, how can we train foundation models of biology so that we can better translate therapeutics from early discovery and preclinical models to patients.” — Ron Alfa “We think the most important thing for any application of machine learning is the data.” — Ron Alfa “The goal here is to train models that can do what humans cannot do, that can understand biology that we haven't discovered yet.” — Ron Alfa “The big aim of Noetik is to develop these [foundational] models for therapeutics discovery.” — Ron Alfa Links: Noetik Octo Virtual Cell (OTCO)
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Foundation Model Series: Accelerating Pathology Model Development Using Embeddings with Julianna Ianni from Proscia | 27 janv. 2025 | 00:20:51 | |
How can foundation models accelerate breakthroughs in precision medicine? In today’s episode of Impact AI, we explore this question with returning guest, Julianna Ianni, Vice President of AI Research and Development at Proscia, a company revolutionizing pathology through cutting-edge technology. Join us as we explore how their platform, Concentriq, and its new Embeddings feature are transforming AI model development, making pathology-driven insights faster and more accessible than ever before. You’ll also learn how Proscia is shaping the future of precision medicine and discover practical insights for leveraging AI to advance healthcare. Whether you're curious about pathology, AI, or innovations in precision medicine, this episode offers invaluable takeaways you won’t want to miss! Key Points:
Quotes: “With the rise of foundation models that are pathology-specific and more powerful than the models of yesterday, the ability to extract embeddings efficiently became even more important for us.” — Julianna Ianni “The pathology world didn't need another hit movie. It needed a streaming service.” — Julianna Ianni “[Continue] to innovate and [understand] what's out there. There's a lot of change in the [pathology] field right now – You're going to make plans and then you're going to need to remake those plans because things are changing so quickly.” — Julianna Ianni “ChatGPT didn't pervade our culture because it's fantastic technology. It pervaded our culture because the fantastic technology was easy to use. Pathology should be that easy. Our aim is to drive it there.” — Julianna Ianni Links: Julianna Ianni on Google Scholar Concentriq Embeddings Previous episode of Impact AI: Data-Driven Pathology with Coleman Stavish and Julianna Ianni from Proscia
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Actionable Soil Insights with Benjamin De Leener from ChrysaLabs | 06 janv. 2025 | 00:20:10 | |
With farmers sometimes waiting weeks for lab results to make critical decisions, Benjamin De Leener, Co-Founder and Chief Science Officer of ChrysaLabs, sought to transform the future of soil health. ChrysaLabs has developed a groundbreaking handheld, AI-powered probe that delivers fast field-ready insights into soil properties like pH, nutrients, and organic matter. In this episode of Impact AI, Benjamin dives into the journey of creating this innovative tool, the challenges of working with complex agricultural data, and the role of machine learning in empowering farmers to make sustainable, data-driven decisions. Tune in to discover how this technology is not only boosting farming efficiency but also contributing to a healthier ecosystem and the fight against climate change! Key Points:
Quotes: “There’s a translation between the light information that we receive from the spectrometer and the information that is actionable for the farmers and agronomists. The machine learning models are between the hardware, the application, and what the farmers can do.” — Benjamin De Leener “The main challenge that the agronomists and the farmers have is the data about what’s in the soil. So, that’s what we provide.” — Benjamin De Leener “The more data you accumulate, the bigger the variability that you need to take into account. It’s not always better to think, ‘The more data I have, the better’ because sometimes, the less data, the more focused the models are.” — Benjamin De Leener “We want to combat climate change – [We believe] that the soil can sequester a lot of carbon through agriculture, and we want to provide a way to measure that so that, when we choose one agronomical practice over another, we understand what we’re doing.” — Benjamin De Leener Links: Benjamin De Leener on LinkedIn Benjamin De Leener on Google Scholar
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Advancing Therapies for Immune Diseases with Kfir Schreiber from DeepCure | 16 déc. 2024 | 00:20:28 | |
Can AI cure autoimmune diseases? This episode of Impact AI dives into the groundbreaking work of DeepCure, where artificial intelligence meets medicinal chemistry to tackle some of healthcare's most stubborn challenges. Co-founder and CEO Kfir Schreiber shares how his team uses advanced machine learning tools, physics simulations, and human expertise to design the next generation of small molecule drugs. From overcoming data limitations to fostering tight collaboration between machine learning scientists and chemists, this discussion illuminates the potential of AI-driven innovation in transforming patient outcomes. With a rheumatoid arthritis drug nearing clinical trials, DeepCure is poised to redefine the future of medicine. Tune in to discover how AI can accelerate drug discovery, overcome data challenges, and create life-changing therapies, as well as how these insights can inspire your own innovative pursuits! Key Points:
Quotes: “Machine learning in our space is almost never a complete solution. It's a way to augment our chemists [and] our biologists [to] try to make them capable of solving problems that were unsolved before.” — Kfir Schreiber “One of the best things about DeepCure [is the] very tight collaboration between the domain experts and our machine learning scientists.” — Kfir Schreiber “Your average machine-learning scientist doesn't have chemistry intuition. We need this feedback and we need to integrate this feedback back into our models to make the predictions make sense.” — Kfir Schreiber “Focus on the problem, focus on the value, and work your way backwards to the best tools to use.” — Kfir Schreiber Links: DeepCure
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Unlocking Unstructured Health Data with David Sontag from Layer Health | 09 déc. 2024 | 00:27:14 | |
What if we could unlock the hidden potential of unstructured health data to improve patient outcomes? In this episode, I sit down with David Sontag, co-founder and CEO of Layer Health, to discuss the transformative role of AI in healthcare. David, an MIT professor (on leave) and leading machine learning researcher, delves into how Layer Health addresses one of healthcare’s most persistent challenges: extracting actionable insights from unstructured medical data. In our conversation, David explains how Layer Health’s AI platform automates complex chart review tasks, tackles data generalization issues across diverse healthcare systems, and overcomes challenges like bias and dataset shifts. We explore Layer Health’s groundbreaking use of large language models (LLMs), the importance of scalable AI solutions, and the integration of AI into clinical workflows. Join us to discover how Layer Health is reducing administrative burdens, improving data accessibility, and shaping the future of AI-powered healthcare with David Sontag. Key Points:
Quotes: “Our vision for Layer Health is to transform healthcare with artificial intelligence, really building upon all of the work that we've been doing over the past decade in the AI and health field and academic space.” — David Sontag “What we realized very quickly is that where [Layer Health] would have the biggest impact was bringing the right information to the physician's fingertips at the right point in time.” — David Sontag “We're using large language models to drive the abstraction of those clinical variables that we need for these either retrospective or prospective use cases.” — David Sontag “Where I think we're going to see the biggest source of bias is likely going to be not along the traditional demographic-related quantities, but rather on more clinical quantities.” — David Sontag “A lot of the friction that we currently see in healthcare, [Layer Health] is going to really take a big bite out of [it].” — David Sontag Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Discovering Protein Drug Candidates with Hanadie Yousef from Juvena Therapeutics | 25 nov. 2024 | 00:19:32 | |
How can advancements in biotechnology and machine learning lead to revolutionary treatments for age-related diseases? In this episode, I speak with Hanadie Yousef, CEO and Co-Founder of Juvena Therapeutics, to discuss her work on protein-based therapeutics. Hanadie, a neurobiologist specializing in aging and tissue degeneration, has pioneered research at Juvena to identify regenerative proteins that can restore tissue function and combat chronic diseases. In our conversation, Hanadie details Juvena’s AI-driven platform that identifies, validates, and engineers protein candidates with therapeutic potential. We explore the power of machine learning models in drug discovery, the challenges of working with multi-omics data, and the potential for new treatments to revolutionize healthcare by targeting disease at the molecular level. Join us to hear how Juvena Therapeutics is setting a new standard in precision medicine aimed at helping individuals age with vitality. Key Points:
Quotes: “Juvena is part of really, a new approach to leveraging the biology of aging and underlying mechanisms associated with why our tissues decline in function, in order to target this biology so that we can treat a broad swath of diseases.” — Hanadie Yousef “That's ultimately the goal of Juvena, to really enable people to age with dignity, to continue to contribute to society, and to really maintain their health until the very end.” — Hanadie Yousef “Ultimately, [machine learning is] leveraged at every stage of the process from in silico prediction, and screening through to the actual engineering and drug development.” — Hanadie Yousef “When it comes to wet lab data generation, sometimes you're really limited by just the quantity of data that you can generate.” — Hanadie Yousef “AI isn't the solution to everything. Oftentimes, you do still want to have that human in the loop and really test the accuracy of these models.” — Hanadie Yousef Links: Juvena Therapeutics on LinkedIn
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Real-World Evidence for Healthcare with Brigham Hyde from Atropos Health | 18 nov. 2024 | 00:11:27 | |
To succeed at an AI startup, you have to be able to show your work and its value. During this episode, I am joined by Brigham Hyde, Co-Founder and CEO of Atropos Health, to talk about his app that gathers real-world evidence for healthcare. He is an entrepreneur, operator, and investor who is deeply immersed in the potential of data and AI. Join us as he shares his journey to creating Atropos Health, why he believes AI is important for healthcare, and the potential it holds to bridge the evidence gap. We discuss how the lack of diversity in healthcare data has impacted patient outcomes leading up to this point and explore some of the methods Atropos uses to get the most out of machine learning. We discuss the AI data-gathering process, how each setup is validated and adapted, and how he measures the impact of his technology. In closing, he shares advice for other leaders of AI-powered startups and offers his vision for the future impact of Atropos. Key Points:
Quotes: “At Atropos, we focus on the automation and generation of high-quality real-world evidence to support clinical decision-making with the dream of creating personalized evidence for everyone.” — Brigham Hyde “We see the role of real-world evidence and observational research as a great way to supplement that gap.” — Brigham Hyde “It's our ability to create that evidence, transparently show you the populations that are being used and the bias that is involved, and the techniques to remove that bias that are the key.” — Brigham Hyde “You've got to be able to show how what you're doing works, that it's not biased, and that it's applicable to the health system you're working with, and it's got to be done in extremely high quality.” — Brigham Hyde Links: Brigham Hyde on X
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| De-Risking Drug Translation with Jo Varshney from VeriSIM Life | 11 nov. 2024 | 00:28:41 | |
As machine learning becomes increasingly widespread, AI holds the potential to revolutionize drug development, making it faster, safer, and more affordable than ever. In this episode, I'm joined by Jo Varshney, Founder and CEO of VeriSIM Life, to explore how her company is transforming drug translation through hybrid AI. With her unique blend of expertise as a veterinarian and computer scientist, Jo leverages biology, chemistry, and machine learning knowledge to tackle the translational gap between animal models and human patients. You’ll learn about VeriSIM Life’s innovative approach to overcoming data limitations, synthesizing new data, and applying ML models tailored to various diseases, from rare conditions to neurological disorders. Jo also reveals VeriSIM’s unique translational index score, a tool that predicts clinical trial success rates and helps pharma companies identify promising drugs early and avoid costly failures. For anyone curious about the future of AI in healthcare, this episode offers a fascinating glimpse into the world of biotech innovation. To discover how VeriSIM Life’s technology is poised to bring life-saving treatments to patients faster and more safely than ever before, be sure to tune in today! Key Points:
Quotes: “[Hybrid AI] helps us not only unravel newer methods and mechanisms of actions or novel targets but also helps us identify better drug candidates that could eventually be safer and more effective in human patients.” — Jo Varshney “Biology is complex. We need to understand it enough to create a codified version of that biology.” — Jo Varshney “If you're just using machine learning-based methods, you may not get the right features to see the accuracy that you would see with the hybrid AI approach that we take.” — Jo Varshney “Focus on validation and showing some real-world outcomes [rather than] just building the marketing outcome because, ultimately, we want it to get to the patients. We want to know if the technology really works. If it doesn't work, you can still pivot.” — Jo Varshney Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Decoding the Immune System for Drug Discovery with Noam Solomon from Immunai | 04 nov. 2024 | 00:18:02 | |
Today’s guest believes that decoding the immune system is at the heart of improving drug efficacy. He is currently focused on this effort as the CEO and Co-founder of Immunai – a company that is building an AI model of the immune system to facilitate the development of next-generation immunomodulatory therapeutics. Noam Solomon begins our conversation by detailing his professional history and how it led to Immunai before explaining what Immunai does and why this work is vital for healthcare. Then, we discover how understanding the immune system will help to improve how drugs work in our bodies, how the team at Immunai accomplishes its goals, the major challenges of working with complex ML models, and some helpful recommendations for processing the high-dimensional nature of biological data. Noam also explains the collaborative landscape of Immunai, how the evolution of technology made his work possible, Immunai’s plans for the future, and his advice to others on a similar career path. Key Points:
Quotes: “First, let’s talk about the problem, which is today, getting a drug from IND approval to FDA approval—which is the process of doing clinical trials—has less than a 10% chance of success, usually about a 5% chance, takes more than 10 years, and more than $2 billion of open immune therapy.” — Noam Solomon “Different people respond differently to the same drug, and the reason they respond differently is because their immune system is different.” — Noam Solomon “You first need to fall in love with the problems. Many ML people—physicists, mathematicians, computer scientists—we love building models; we love solving puzzles. In biology, you need to really fall in love with the question you are trying to answer.” — Noam Solomon “It’s a great decade for biology.” — Noam Solomon Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Foundation Model Series: Accelerating Radiology with Robert Bakos from HOPPR | 28 oct. 2024 | 00:28:42 | |
Imagine a world where radiology backlogs are a thing of the past, and AI seamlessly augments the expertise of radiologists. Today, I'm joined by Robert Bakos, Co-Founder and CTO of HOPPR, to discuss how his company is bringing this vision to life. HOPPR is pioneering foundation models for medical imaging that have the potential to transform healthcare. With access to over 15 million diverse imaging studies, HOPPR is developing multimodal AI models that tackle radiology’s most significant challenges: high imaging volumes, limited specialist availability, and the growing demand for rapid, accurate diagnostics. In this episode, Robert offers insight into the rigorous process of training these models on complex data while ensuring they integrate seamlessly into medical workflows. From data partnerships to specialized clinical collaboration, HOPPR’s approach sets new standards in healthcare AI. To discover how foundation models like these are revolutionizing radiology and making healthcare more efficient, accessible, and equitable, be sure to tune in today! Key Points:
Quotes: “Having clinical collaboration is super important. At HOPPR, our clinicians are an important part of our product development team – They're absolutely vital for helping us evaluate the performance of the model.” — Robert Bakos “Because we are training across all these different modalities, getting access to this data can be challenging. Having great partnerships is critical for finding success in this space.” — Robert Bakos “Make sure that you're addressing real problems. There are a lot of great ideas and cool things you can implement with AI, but at the end of the day, you want to make sure you can deliver value to your customers.” — Robert Bakos “Foundation models – trained on a breadth of data – can make a positive impact on underserved areas around the world. With the volume of images growing so rapidly, constraints on radiologists, and burnout, it's important to leverage these models to make a big impact.” — Robert Bakos Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Optimizing Data Center Operations with Vedavyas Panneershelvam from Phaidra | 21 oct. 2024 | 00:22:14 | |
What are the unique challenges of operating mission-critical facilities, and how can reinforcement learning be applied to optimize data center operations? In this episode, I sit down with Vedavyas Panneershelvam, CTO and co-founder of Phaidra, to discuss how their cutting-edge AI technology is transforming the efficiency and reliability of data centers. Phaidra is an AI company that specializes in providing intelligent control systems for industrial facilities to optimize performance and efficiency. Vedavyas is a technology entrepreneur with a strong background in artificial intelligence and its applications in industrial and operational settings. In our conversation, we discuss how Phaidra’s closed-loop, self-learning autonomous control system optimizes cooling for data centers and why reinforcement learning is the key to creating intelligent systems that learn and adapt over time. Vedavyas also explains the intricacies of working with operational data, the importance of understanding the physics behind machine learning models, and the long-term impact of Phaidra’s technology on energy efficiency and sustainability. Join us as we explore how AI can solve complex problems in industry and learn how Phaidra is paving the way for the future of autonomous control with Vedavyas Panneershelvam. Key Points:
Quotes: “Phaidra is like a closed-loop self-learning autonomous control system that learns from its own experience.” — Vedavyas Panneershelvam “Data centers basically generate so much heat, and they need to be cooled, and that takes a lot of energy, and also, the constraints in that use case are very, very narrow and tight.” — Vedavyas Panneershelvam “The trick [to validation] is finding the right balance between relying on the physics and then how much do you trust the data.” — Vedavyas Panneershelvam “[Large Language Models] have done a favor for us in helping the common public understand the potential of these, of machine learning in general.” — Vedavyas Panneershelvam Links: Vedavyas Panneershelvam on LinkedIn
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Structuring Medical Text with Tim O'Connell from Emtelligent | 14 oct. 2024 | 00:18:02 | |
What if AI could unlock the potential of healthcare’s vast, unstructured data? In this episode, Tim O'Connell, Co-Founder and CEO of Emtelligent, explains how his company is bridging the gap between messy medical data and usable insights with AI-powered solutions. Drawing from his background in both engineering and radiology, Tim discusses how he saw firsthand the inefficiencies caused by disorganized medical notes and reports, which led to the creation of Emtelligent. He breaks down how their AI models work to process and structure this data, making it usable for healthcare professionals, researchers, and beyond. Tim also dives into the technical challenges, from handling faxed medical records to ensuring high levels of precision and recall in model training. Beyond the technology, he emphasizes the importance of safety, ethical use, and how Emtelligent continues to adapt its AI to meet the evolving needs of the healthcare industry, helping to make patient care more efficient and accurate. Don’t miss out on this important conversation with Tim O’Connell from Emtelligent! Key Points:
Quotes: “During that year [that I was] working in the hospital, – I saw so many problems that we have in the healthcare environment and realized that quite a few of them had to do with the fact [that] we deal with so much unstructured data.” — Tim O’Connell “Every time a human goes to see a caregiver, some kind of an unstructured text note is generated – We really can't use a lot of that data, unless it's another human who's reading that data.” — Tim O’Connell “I’m still a practicing radiologist. – It’s not just a matter of intelligent people coming up with good ideas and going, ‘Oh, well. [Let’s throw this] against the wall and see what sticks’. We're developing solutions that are applicable in today's healthcare environment.” — Tim O’Connell Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Foundation Model Series: Enabling Digital Pathology Workflows with Dmitry Nechaev from HistAI | 07 oct. 2024 | 00:29:32 | |
What happens when you combine AI with digital pathology? In this episode, Dmitry Nechaev, Chief AI Scientist and co-founder of HistAI, joins me to discuss the complexity of building foundation models specifically for digital pathology. Dmitry has a strong background in machine learning and experience in high-resolution image analysis. At HistAI, he leads the development of cutting-edge AI models tailored for pathology. HistAI, a digital pathology company, focuses on developing AI-driven solutions that assist pathologists in analyzing complex tissue samples faster and more accurately. In our conversation, we unpack the development and application of foundation models for digital pathology. Dmitry explains why conventional models trained on natural images often struggle with pathology data and how HistAI’s models address this gap. Learn about the technical challenges of training these models and the steps for managing massive datasets, selecting the correct training methods, and optimizing for high-speed performance. Join me and explore how AI is transforming digital pathology workflows with Dmitry Nechaev! Key Points:
Quotes: “Regular foundation models are trained on natural images and I'd say they are not good at generalizing to pathological data.” — Dmitry Nechaev “In short, [a foundational model] requires a lot of data and a lot of [compute power].” — Dmitry Nechaev “Public benchmarks [are] a really good thing.” — Dmitry Nechaev “Our foundation models are fully open-source. We don't really try to sell them. In a sense, they are kind of useless by themselves, since you need to train something on top of them, so we don't try to profit from these models.” — Dmitry Nechaev “The best lesson is that you need quality data to get a quality model.” — Dmitry Nechaev “[HistAI] don't want AI technologies to be a privilege of the richest countries. We want that to be available around the world.” — Dmitry Nechaev Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Foundation Model Series: Creating Small Molecules for Drug Discovery with Jason Rolfe from Variational AI | 30 sept. 2024 | 00:29:29 | |
Building on the trends in language processing, domain-specific foundation models are unlocking new possibilities. In the realm of drug discovery, Jason Rolfe is spearheading innovation at the intersection of AI and pharmaceuticals. As the Co-Founder and CTO of Variational AI, Jason leads a platform designed to generate novel small molecule structures that accelerate drug development. In this episode, he delves into how Variational AI uses foundation models to predict and optimize small molecules, overcoming the immense complexity of drug discovery by leveraging vast datasets and sophisticated computational techniques. He also addresses the key challenges of modeling molecular potency and why traditional machine-learning approaches often fall short. For anyone curious about AI's impact on healthcare, this conversation offers a fascinating look into cutting-edge innovations set to reshape the pharmaceutical industry. Tune in to find out how the types of breakthroughs we discuss in this episode could revolutionize drug development, bring new therapeutics to market across disease areas, and positively impact lives! Key Points:
Quotes: “Rather than forming individual models for specific drug targets, we're creating a joint model over hundreds, eventually thousands of drug targets.” — Jason Rolfe “Data quality is essential. In particular, if you're drawing from multiple different data sources, frequently, those sources aren't commensurable.” — Jason Rolfe “If you don't have a proven track record where people are already throwing money at you, it is very challenging to try to bring a new technology from the drawing board into commercial application using venture funding.” — Jason Rolfe “Whenever you're developing a new technology or product, you need to test early and often. Some of your intuitions will be good. Most of your intuitions will be a waste of time – The more quickly you can distinguish between those two classes, the more efficiently you can move toward success.” — Jason Rolfe Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Foundation Model Series: Building New Materials for Climate with Jonathan Godwin from Orbital Materials | 23 sept. 2024 | 00:25:03 | |
AI is unlocking the future of materials science and today’s guest Jonathan Godwin, co-founder and CEO of Orbital Materials, is at the forefront of this transformation. With a background in AI research and experience leading groundbreaking projects at Google-owned DeepMind, Jonathan is now applying machine learning to develop advanced materials that can drive decarbonization. In this episode, he explains how Orbital Materials is using foundation models (like ChatGPT for language or MidJourney for images) to design new materials that capture carbon, store energy, and improve industrial efficiency. He also shares insights into the company’s mission, the challenges of simulating atomic-level interactions, and why open-sourcing their model, Orb, is crucial for innovation. To discover how AI is revolutionizing the fight against climate change and learn how these cutting-edge materials could shape a more sustainable future, don’t miss this inspiring conversation with Jonathan Godwin! Key Points:
Quotes: “We develop materials that can capture CO2 from specific gas streams – coming out of an industrial facility, new energy storage technologies that allow – [data centers] to operate behind the meter, or ways to improve the water efficiency of a data center or industrial facility.” — Jonathan Godwin “Foundation models are the crux of how we're able to leverage AI in this day and age. If you want to [say], 'We're pushing the limits of what AI is able to do. We're leveraging the most recent breakthroughs,' – you've got to be building foundation models or using foundation models.” — Jonathan Godwin “AI is a massively powerful creativity aid and accelerant. We’ve seen that in other areas of AI and we're bringing that to advanced materials.” — Jonathan Godwin Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Foundation Model Series: Understanding Brain Activity with Dimitris Sakellariou from Piramidal | 16 sept. 2024 | 00:23:41 | |
What if we could understand brain activity in real-time to better diagnose neurological conditions? In this episode, part of a special mini-series on domain-specific foundation models, I sit down with Dimitris Sakellariou, the founder and CEO of Piramidal, to talk about their groundbreaking work in automating EEG interpretation. Piramidal is focused on democratizing brain health insights, making interpreting brainwave data more accessible and accurate. With a strong foundation in neuroscience and AI, Dimitris and his team are developing models that could revolutionize how we understand brain activity and diagnose neurological conditions. In our conversation, Dimitris explains the challenges of building a foundation model for brain activity, the role of data diversity, and the future potential for personalized brain health monitoring. Discover the implications of Piramidal’s technology beyond healthcare and its application in cognitive enhancement and stress management. Tune in as we explore how Piramidal is paving the way for personalized brain health monitoring and why this could be a game-changer for the future of medicine! Key Points:
Quotes: “Piramidal is primarily focused at the moment in automating, or otherwise democratizing the interpretation of these tests, these brainwave recordings so that patients and people that have issues with their brain can get access to the diagnosis much, much, much faster.” — Dimitris Sakellariou “It's very important to have discussions with neuroscientists and clinical experts in order to understand what is the end-to-end pipeline from receiving data all the way to inference.” — Dimitris Sakellariou “Finding the right person. Someone that is very keen to build together with you and make important and difficult decisions can change massively a trajectory of your company.” — Dimitris Sakellariou Links: Dimitris Sakellariou on LinkedIn
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Foundation Model Series: Better, Faster, Cheaper Earth Observation with Bruno Sánchez-Andrade Nuño from Clay | 09 sept. 2024 | 00:35:35 | |
Can AI be applied to enhance geospatial data for climate, nature and people? This episode kicks off a miniseries about domain-specific foundation models. Following the trends in language processing, domain-specific foundation models are enabling new possibilities for a variety of applications, including Earth observation. During this conversation, I am joined by Bruno Sánchez-Andrade Nuño, Executive Director of Clay, a nonprofit organization harnessing the power of AI for satellite images, spatial data, and more. Bruno shares the functionality and concept behind Clay, and his journey to building it. He goes on to unpack the tool’s foundation model in broad strokes, before explaining why it's important, and sharing the challenges he has faced along the way. We discuss the legal aspects of building Clay, and it’s primary goal to make it as easy as possible for any user to achieve their goals. We also touch on what the future might hold for Clay and the future of Earth observation. Thanks for listening! Key Points:
Quotes: “Clay is trying to figure out how to finally increase the adoption of remote sensing by leveraging a tool that itself is very complex, but the result of that tool is very easy to use.” — Bruno Sánchez-Andrade Nuño “If you start with a foundational model that gets you most of the way there, [then] you can create those trials much quicker, much cheaper, and much more environmentally friendly.” — Bruno Sánchez-Andrade Nuño “This is so new, we get the chance, those of us working on it, that we can save the whole industry, if you will, the whole space of AI for it.” — Bruno Sánchez-Andrade Nuño “Clay, I believe, is not only the largest and most efficient model AI for Earth, for any kind of like foundational model. It is also completely open source.” — Bruno Sánchez-Andrade Nuño “What we try to focus on is how can we make it as simple as possible for anyone anywhere to use this model for anything they want to do.” — Bruno Sánchez-Andrade Nuño Links: Bruno Sánchez-Andrade Nuño on X Bruno Sánchez-Andrade Nuño on LinkedIn
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Evolutionary Insights for Drug Discovery with Ashley Zehnder from Fauna Bio | 02 sept. 2024 | 00:27:17 | |
In a world where conventional drug discovery methods frequently fall short, today's guest addresses the critical challenge of fighting human diseases by drawing inspiration from nature’s most resilient creatures. Could the secret to overcoming our most stubborn illnesses lie in the extraordinary adaptability of extreme mammals? Veterinarian-scientist Ashley Zehnder, the Co-founder and CEO of AI-driven drug discovery company Fauna Bio, believes so. By leveraging data from 100 million years of evolved disease resistance in mammals, Ashley sees a unique opportunity at the crossroads of genomics and emerging model species to improve health for all species, including humans. In this episode, she explores how harnessing the biological secrets of these animals using AI and machine learning could revolutionize medicine, leading to breakthroughs that benefit us all. Tune in to discover how Fauna Bio is pioneering a new frontier in drug discovery and how understanding the resilience of these creatures could reshape the future of healthcare! Key Points:
Quotes: “[Fauna Bio uses] AI and genomics as a way to identify the most impactful targets for new therapeutic programs across a broad number of diseases.” — Ashley Zehnder “It’s certainly easier than it has been in the past to generate very high-quality single-cell RNA sequencing. We’re doing a lot of that. The challenges on the technical side are getting much easier. The challenges on the interpretation side are still there.” — Ashley Zehnder “There are many points along the drug discovery path where AI companies can differentiate. But that story has to be clear because, otherwise, it's very hard to get out of the signal-to-noise that is the AI discovery landscape in biopharma” — Ashley Zehnder Links: Science Issue dedicated to the Zoonomia Project
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Better Therapeutics Using Lab-Grown Tissue with Andrei Georgescu from Vivodyne | 26 août 2024 | 00:33:57 | |
One of the biggest hurdles in medical research is the gap between animal studies and human trials, a disconnect that often leads to failed drug tests and wasted resources. But what if there was a way to bridge that gap and create treatments that are more effective for humans from the start? Today, I am joined by Dr. Andrei Georgescu, Founder and CEO of Vivodyne, a groundbreaking biotechnology company that is transforming how scientists study human biology and develop new therapeutics. In this episode, he reveals how Vivodyne harnesses lab-grown tissue and advanced multimodal AI to create more effective therapeutics. We explore the challenges of gathering human tissue data, the collaboration between biologists, robotics engineers, and machine learning developers to build powerful machine learning models, and the profound impact that Vivodyne is poised to make in the fight against diseases. To discover how Vivodyne’s innovations can lead to more successful treatments and faster drug development, tune in today! Key Points:
Quotes: “Vivodyne grows human tissues at a very large scale so that we can understand human physiology and we can test directly on it in order to discover and develop better drugs that are both safer and more efficacious.” — Andrei Georgescu “We use machine learning and AI as a mechanism to understand the complexity of very deep data and to very efficiently apply that complexity and infer from what we've learned across the very large breadth of data that we collect.” — Andrei Georgescu “To address [the problem of a] glaring lack of trainable data, we create that data by growing it at scale.” — Andrei Georgescu “If you're a technical founder, do something that is incredibly hard because the ability for you to do that thing will grant you much more leverage than creating what is otherwise a much more simple and generic business.” — Andrei Georgescu “[With Vivodyne], we will enter a world of plenty where the development of new drugs against diseases becomes a far more successful, reliable, and predictive process, and we're able to make much safer and much more effective drugs just by virtue of being able to optimize that therapeutic on human tissues before giving it to people for the first time in-clinic.” — Andrei Georgescu Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Accelerating Regenerative Agriculture with Marie Coffin from CIBO Technologies | 19 août 2024 | 00:16:02 | |
Marie Coffin is the Vice President of Science and Modeling at CIBO Technologies, and she is with me today to discuss regenerative agriculture. Join us as we explore CIBO’s work to influence company carbon footprints across industries, and how machine learning supports this process through remote sensing. Delving deeper, Marie unpacks how satellite imagery integrates with their computer vision system for a more scalable solution. Next, we discuss obtaining and categorizing data in the US, exploring some of the obstacles that stem from privacy and data protection concerns. We touch on data quality and discuss the reason behind the geographical parameters they have applied to the work before Marie shares her approach to collaborating with external experts and agronomists. She offers her advice for startups in the tech space, emphasizing creating value for your clients over keeping up with trends, predicts the future endeavors that CIBO will focus on, and more. Thanks for listening! Key Points:
Quotes: “It’s pretty straightforward to estimate the carbon footprint of a single farmer’s field or even the carbon footprint of a whole farm, but, to make an impact, we need to be able to scale that across the landscape.” — Marie Coffin “That is really the biggest challenge; it’s just getting enough data.” — Marie Coffin “When you’re working in a really cutting-edge area, it’s tempting to sort of get caught up in the buzz of the new technology and lose sight of what the customer needs.” — Marie Coffin “We need to not always be following the latest, greatest advance. We need to be going in a direction that’s going to really provide value.” — Marie Coffin Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Measuring Biodiversity Using Insects with Mads Fogtmann from Fauna Photonics | 12 août 2024 | 00:20:34 | |
What if technology could be the key to averting a biodiversity crisis? Today, I explore this possibility with Mads Fogtmann, Chief Data Officer of FaunaPhotonics, as we discuss their groundbreaking approach to biodiversity monitoring. I talk with Mads about the looming biodiversity crisis, the innovative solutions his team is developing to address the urgent need for scalable biodiversity monitoring, and the central role that humans have to play in all this. Find out how the FaunaPhotonics platform is employing advanced sensing technology and machine learning to protect ecosystems, why insects are such useful proxies for monitoring ecosystem health, and their successful partnerships with other domain experts and researchers. Our conversation also covers the broader implications of biodiversity loss, the role of public awareness in conservation, and the future of biodiversity monitoring. Join us for a comprehensive and insightful discussion on how technology can help safeguard our planet's future and ensure the stability of natural and human systems alike! Key Points:
Quotes: “The clothes we wear, the food we eat, the water we drink, the material we use to build houses: everything comes from nature. And right now, we are destroying that foundation rapidly.” — Mads Fogtmann “I think it’s important that we become more aware that we are an integral part of nature.” — Mads Fogtmann “If you can’t measure it, then how can you protect the rights? – [We come with the solution] that allows them to measure [the impact on biodiversity] so they can protect it. We do this by using insect sensing. The reason we do this is that insects are so fundamental to the ecosystem.” — Mads Fogtmann “Insects are the best proxy that you can have for actually measuring the health of [an] ecosystem.” — Mads Fogtmann “There’s a huge need and an interest in ‘how we can actually scale biodiversity monitoring to kind of help us understand what’s going on with nature at the moment.’” — Mads Fogtmann Links: Mads Fogtmann on LinkedIn
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Optimizing Manufacturing with Berk Birand from Fero Labs | 05 août 2024 | 00:20:50 | |
Manufacturing is a fundamental part of our economy. Unfortunately, a huge swath of the industry is still dependent on outdated methods, adversely impacting our environment. To address these challenges, one company is harnessing the power of AI to transform traditional manufacturing, driving unprecedented efficiency and sustainability in the industry. Joining me today is Berk Birand, co-founder and CEO of Fero Labs, to unpack how AI is optimizing the manufacturing sector. Tuning in, you'll learn all about Fero Labs' innovative software and how it’s empowering engineers in industries like steel and chemicals to harness machine learning, drastically reducing waste and energy consumption. We discuss how their AI analyzes historical production data to ensure factories operate at peak performance and how this is boosting sustainability and profitability. Our conversation also unpacks the critical role of explainable AI in building trust within the industrial sector, where precision and reliability are essential. Tune in to discover how Fero Labs is paving the way for a greener industrial future! Key Points:
Quotes: "One of our largest customers was able to reduce the waste of raw materials, about a million pounds just throughout last year, by using our software AI system." — Berk Birand "We think AI will play a key role in the transition to a green economy." — Berk Birand
"In an environment like this, an engineer in a factory would just not want to use a software that they don't trust, because ultimately, it's their job that's on the line." — Berk Birand
Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| More Successful IVF with Daniella Gilboa from AIVF | 29 juil. 2024 | 00:27:38 | |
In this episode of Impact AI, we delve into the transformative impact of AI on in-vitro fertilization (IVF) with Daniella Gilboa, co-founder and CEO of AIVF, a startup that develops AI-powered IVF solutions to help increase the certainty of a successful journey to parenthood. Join me as Daniella shares her mission to democratize fertility care and offers insight into AIVF’s proprietary technology that delivers reliable, objective, and data-driven IVF outcomes for clinicians, embryologists, and patients. We explore the role and challenges of machine learning at AIVF, strategies for validating AI models in clinical practice, and the current demand for AI-powered IVF solutions. We also discuss the metrics used to measure the impact of AIVF's technology, Daniella’s advice for other AI-powered startup leaders, and her vision for the future. Tune in to gain valuable insights into the future of fertility care and find out how AI is making IVF more effective and accessible! Key Points:
Quotes: “We showed that if you use AI as a tool for the embryologist – [it] increased the success rates – The decision-making is faster, more accurate. You freeze less embryos because each embryo you freeze is accurate – It changes the way the lab works and it optimizes everything.” — Daniella Gilboa “The way you interact with the patient and consult the journey ahead is changing. It’s more accurate. It allows you to make more informed decisions. This is the right way of doing medicine. It needs to be data-driven rather than subjective human analysis.” — Daniella Gilboa “AIVF needs to become the standard of care.” — Daniella Gilboa Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Vision Intelligence Filters with Kit Merker from Plainsight Technologies | 22 juil. 2024 | 00:28:13 | |
Image-based machine learning is fast becoming an AI staple, and with its new Vision Intelligence Filters, Plainsight Technologies is staking its claim as an industry pioneer. Today, I am joined by Plainsight CEO, Kit Merker, who is here to share all the details behind his company’s latest innovation. Kit begins by explaining what Plainsight does and why this work matters in the AI realm. Then, we learn about the mechanics behind Plainsight’s Vision Intelligence Filters, the company’s ML models and data protocols concerning existing customers, the ins and outs of bringing a product like the Vision Intelligence Filters to life, and how bias manifests in image-trained models. We also discuss the most game-changing applications that Kit has been involved in, and he shares some critical advice for young leaders of AI-powered startups, plus so much more! Key Points:
Quotes: “Our goal is to give customers very high accuracy on their models.” — Kit Merker “A lot of times, traditional enterprises are looking for a solution or an app. The filter is like an app, and so customers can start really small with us, get an app that they trust the data, and then expand from there. They don't have any machine learning expertise required.” — Kit Merker “Don't fake your demos!” — Kit Merker Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Interpreting Infant Cries with Charles Onu from Ubenwa Health | 15 juil. 2024 | 00:22:53 | |
Infants cry when they're hungry, tired, uncomfortable, or upset. They also cry when they’re in pain or severely ill. But how can parents tell the difference? To help us address this critical question, I'm joined by Charles Onu, a health informatics researcher, software engineer, and CEO of Ubenwa. Ubenwa is a groundbreaking app that uses AI to interpret infants' needs and health by analyzing the biomarkers in their cries. Charles conceived of the idea while working in local communities in south-eastern Nigeria, where high rates of newborn mortality due to late detection of Perinatal Asphyxia inspired him to create a solution. In this episode, Charles shares insights into Ubenwa's machine-learning models and how they establish an infant's cry as a vital sign. He discusses the process of collecting and annotating data through partnerships with children's hospitals, the challenges of working with audio data, the benefits of creating a foundation model for infant cries, and much more. He also offers human-focused advice for leaders of AI-powered startups and reflects on his vision for success and the impact he hopes to achieve with Ubenwa. Tune in to discover how understanding your infant’s cries can transform healthcare and well-being for newborns and their families! Key Points:
Quotes: “Ubenwa was born out of the idea that, if there's something that [human doctors] can listen to to come to a conclusion [about an infant’s health], then there has to be something machines can also learn from the infant's cry.” — Charles Onu “The real leap we made with self-supervised learning is that you now do not need an external annotation to learn. The model can use the data to supervise itself.” — Charles Onu “AI-powered or not, – the problem of a startup remains the same. It’s to meet a need that humans have. – At the end of the day, AI is not just there for AI only. It’s only going to be a successful and useful startup if you identify a need and [solve] that problem.” — Charles Onu “Human babies have evolved to communicate their needs and their health through their cries. We [haven’t] had the tools to understand that. Babies have been trying to talk to us for a long time. It's time to listen.” — Charles Onu Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Remote Monitoring and Water Forecasting with Marshall Moutenot from Upstream Tech | 08 juil. 2024 | 00:26:35 | |
Innovative AI technologies are paving the way for more efficient and impactful environmental monitoring. Joining me today to discuss remote monitoring and water forecasting is Marshall Moutenot, the co-founder and CEO of Upstream Tech. From using satellite imagery to monitor conservation projects to employing machine learning for accurate water flow predictions, Upstream Tech is at the forefront of leveraging technology to address environmental challenges. In our conversation, Marshall shares his journey from a tech-savvy childhood to co-founding a company with a mission to make environmental monitoring scalable and cost-effective. He delves into the development of Upstream Tech's two primary products: Lens, for remote monitoring of climate solutions, and HydroForecast, which uses AI to predict water flow, aiding in hydropower management. Marshall also underscores the need for integrating domain knowledge with machine learning to create reliable models before offering practical insights for AI startups. Tune in to learn more about how AI can revolutionize environmental conservation! Key Points:
Quotes: “[The] adaptability of these models is something that is really exciting for the field overall." — Marshall Moutenot
“As an organization, one of [Upstream Tech’s] purposes is to see the 100% renewable grid become a reality. We want to continue to contribute to that and to build forecasts that enable that future.” — Marshall Moutenot Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Scaling Healthcare Through Virtual Primary Care with Anitha Kannan from Curai | 01 juil. 2024 | 00:22:40 | |
What will it take to bring affordable, accessible, and timely healthcare to all? Curai, an AI-powered virtual clinic, is on a mission to do just that by leveraging AI to enhance the efficiency of licensed physicians through text-based virtual primary care. In today’s episode, I sit down with Anitha Kannan, head of AI and founding member of Curai, to talk about the transformative potential of virtual primary care and its role in scaling healthcare access. In our conversation, Anitha delves into the technical aspects of using large language models for patient data processing, the challenges of training models with clinical data, and the strategies Curai employs to ensure high-quality care. We also discuss the innovative ways Curai integrates AI into healthcare, the significance of multidisciplinary teams, and Anitha’s vision for the future of virtual care. Tune in for an insightful conversation on scaling healthcare through virtual primary care and learn how Curai is making a real impact! Key Points:
Quotes: "Our mission is to provide the best health care to everyone." — Anitha Kannan “Today, [Carai runs] a text-based virtual primary care practice. We have our licensed physicians or experts in their fields. Then we supercharge them and bring about a lot of efficiencies by leveraging AI.” — Anitha Kannan "It's very easy to build 80% of a good product with AI today, but I think to get it to 100%, [and] to get it to scale, to be useful in [the] real world — evaluation is the number one thing." — Anitha Kannan “At Curai, the AI team is composed of clinical experts, subject matter experts, researchers, and machine learning engineers. Every project, long-term or short-term, has a mix of these types of expertise in it. This allows us to work through the problem much more effectively.” — Anitha Kannan Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Better EV Batteries with Jason Koeller from Chemix | 24 juin 2024 | 00:27:30 | |
Batteries are arguably the most important technological innovation of the century, powering everything from mobile phones to electric vehicles (EVs). Unfortunately, most batteries have a significant impact on the environment, requiring increasingly scarce and valuable resources to manufacture and typically not designed for easy repair, reuse, or recycling. Today on Impact AI, I'm joined by Jason Koeller, Co-Founder and CTO of Chemix, to find out how his company is leveraging AI to create better, more sustainable EV batteries that could reduce our reliance on elements like lithium, nickel, and cobalt, all without compromising vehicle performance. For a fascinating conversation with a data-driven physicist working at the intersection of software, machine learning, chemistry, and materials science, be sure to tune in today! Key Points:
Quotes: “All data analysis and decision-making is automated by our AI system. This includes analyzing terabytes of battery test data each day.” — Jason Koeller “Looking at broad trends, [electric vehicles (EVs)] and AI have both become [things] that people have been talking a lot more about in the past 10 years and even more so in the past four or five years, and that has happened simultaneously.” — Jason Koeller “Why is everyone not buying an EV? It's largely because they're too expensive or because people are worried they're not charging fast enough or they don't hold enough range for long road trips. – Improving any one of these metrics would be a measure of impact.” — Jason Koeller Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. | |||
| Personalized Cancer Treatment Decisions with Nathan Silberman from Artera | 17 juin 2024 | 00:17:10 | |
Being given a cancer diagnosis is one of the worst pieces of news you can receive as a patient. This is often made even more difficult by the fact that choosing a treatment option is rarely simple or easy. Clinicians need to make multiple assessments before they can move forward, and even then it is often difficult or impossible to make unambiguous predictions. That’s where Artera comes in, a company using multimodal AI tests to provide individualized results for cancer patients, which enables clinicians and patients to make personalized treatment decisions, together. I am joined today by Nathan Silberman, Vice President of Machine Learning and Engineering at Artera, to talk about how Artera’s technology is paving the way for personalized cancer treatment decisions. Join us today, as we get into how Artera is contributing to the cancer treatment process, some of the biggest challenges they face, and how they are addressing these through specifically trained algorithms and robust validation protocols. Be sure to tune in to this important conversation on how Artera is impacting cancer treatment outcomes for the better! Key Points:
Quotes: “Which therapy to choose is simply not an easy choice. Clinicians would ideally be able to accurately assess a patient's risk of a cancer spreading, or adversely affecting the patient's health in the short term. But often, that's hard or impossible for a clinician to predict.” — Nathan Silberman “Clinicians have been wanting and waiting for tools that can predict whether or not a therapy will work for that particular patient. This is ultimately where Artera steps in.” — Nathan Silberman “Rather than wait a month, Artera's test provides the answer within two to three days after the lab receives the biopsy slide. And it is so rewarding to hear from clinicians, and especially patients about the relief we can provide by giving clarity sooner.” — Nathan Silberman “I think the biggest piece of advice I can give is really just making sure that you're laser-focused on the ultimate goal of patient impact.” — Nathan Silberman Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model. | |||
| Faster Object Search with Corey Jaskolski from Synthetaic | 10 juin 2024 | 00:27:12 | |
What if there was a way to revolutionize image-based AI, eliminating the need for extensive prework? In this episode, I sit down with Corey Jaskolski, Founder and President of Synthetaic, to talk about finding objects in images and video quickly. Synthetaic is redefining the landscape of data analysis with its groundbreaking technology that eliminates the need for time-consuming human labeling or pre-built models. It specializes in the rapid analysis of large, unlabeled video and image datasets. In our conversation, we delve into the groundbreaking technology behind Synthetaic's flagship product and how it is revolutionizing image and video processing. Explore how it utilizes an unsupervised backend to swiftly analyze and interpret data, how it is able to work with any kind of image data, and the process behind ingesting and embedding image objects. Discover how Synthetaic navigates biased data and leverages domain expertise to ensure accurate and ethical AI solutions. Gain insights into the gaps holding AI’s application to images back, the different ways the company’s technology can be applied, the future development of Synthetaic, and more! Key Points:
Quotes: “We think about the machine learning problems a little bit differently, because we're not labeling data to go ahead and build a bespoke frozen traditional AI model.” — Corey Jaskolski “We take this very broad view of objects where anything that could be discrete from anything else in the imagery gets called an object, at the risk of basically finding, if you will, too many objects.” — Corey Jaskolski “We think of RAIC as something that solves the cold start problem really well.” — Corey Jaskolski “By and large, we're training image and video-based AIs the same way. We need a paradigm shift that really allows AI to be the force multiplier that it can be.” — Corey Jaskolski Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model. | |||
| Digital Twins for Clinical Trials with Charles Fisher from Unlearn AI | 03 juin 2024 | 00:30:19 | |
What if AI could improve the outcomes of clinical trials by making them more efficient and reducing the number of patients receiving placebos? Well, today’s guest, Charles Fisher is here to tell us all about how his company, Unlearn AI, is creating digital twins to do just that! In this conversation, you’ll hear all about Charles' academic background, what made him decide to create Unlearn AI, what the company does, and how they work within clinical trials. We delve into the problems they focus on and the data they collect before Charles tells us about their zero-trust solution. We even discuss Charles’ opinions of how domain knowledge should be used in machine learning. Finally, our guest shares advice for leaders of AI-powered startups. To hear all this and even find out what to expect from Unlearn in the near future, tune in now! Key Points:
Quotes: “[Unlearn is] typically working on running clinical trials where we might be able to reduce the number of patients who get the placebo by somewhere like – 50%.” — Charles Fisher “[Unlearn] can prove that these studies produce the right answer, even though they leverage these AI algorithms.” — Charles Fisher “It's very difficult to find examples where you can actually have a zero-trust application of AI. I actually don't know of another one besides [Unlearn’s].” — Charles Fisher Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model. | |||
| Cutting Carbon in Concrete with Mathieu Bauchy from Concrete.ai | 27 mai 2024 | 00:30:42 | |
Did you know that concrete is the second most-used material in the world after water? Although it has largely defined modern society, concrete has a hidden climate cost: it is responsible for 1.6 billion tons of carbon dioxide entering the atmosphere annually. For context, that’s more than the entire aviation industry! With these statistics in mind, today’s guest is on a mission to decarbonize the construction industry. As the CTO and co-founder of cleantech startup, Concrete.ai, Mathieu Bauchy is using his expertise in artificial intelligence and materials modeling to prescribe new concrete formulations that are less carbon-intensive and more economical. Today, Mathieu joins me to offer insight into Concrete.ai's exciting technology, why it’s important for the planet, and how it can reduce concrete emissions by a third while also ensuring that concrete producers maximize margins and streamline their supply chains. To find out how this is possible without any changes to the raw materials, no modification of the production process, and no cost premium, be sure to tune in today! Key Points:
Quotes: “Concrete is responsible for 8% of the total CO2 emissions in the world. To give you some context, that's about three times more emissions than the entire aviation industry.” — Mathieu Bauchy “We think that it's the right time for the concrete industry to benefit from what AI can offer to avoid waste during the production of concrete. The idea is that, if we adopt these new technologies, then we can continue to improve our quality of life.” — Mathieu Bauchy “It's not like we are changing the way concrete is made. It's still made in the same plant. It's still made using the same materials. We are just changing the recipe, and just that [can] save about a third of the emissions of concrete.” — Mathieu Bauchy “AI also comes with its own carbon footprint and, to some extent, also contributes to climate change. We should think about how we use AI to solve climate change and not further contribute to it.” — Mathieu Bauchy Links: Concrete.ai
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model. | |||
| Decoding Pathology for Precision Medicine with Maximilian Alber from Aignostics | 20 mai 2024 | 00:19:35 | |
Today, I am joined by Maximilian Alber, Co-founder and CTO of Aignostics, to talk about pathology for precision medicine. You’ll learn about Aignostics’s mission, how they are impacting healthcare, and the transformative power of foundational models. Max explains how Aignostics is driven by the belief that machine learning and data science will help improve healthcare before expanding on the role of foundational models. He describes how they built their foundational model, what sets it apart from other models, and why diversity in their datasets is key. He also breaks down how foundational models have allowed them to develop other models more quickly and better navigate explainability with concepts that are challenging for machine learning. We wrap up with Max’s advice for leaders of other AI-powered startups and where he expects Aignostics will be in the next five years. Tune in now to learn all about foundational models and the innovative work being done at Aignostics! Key Points:
Quotes: “Our mission is to turn biomedical data into insights.” — Maximilian Alber “Everything we do is driven by the belief that machine learning and data science will help us improve healthcare.” — Maximilian Alber “A foundation model is a model that can be used as a starting point for building a machine learning application, with the promise that the foundation model already has a great understanding of the domain.” — Maximilian Alber “We are in active discussions for licensing our foundation model to other companies in order to enable their development as well. [What’s] important here is that we develop our foundation model along regulatory requirements, which will allow it to be used in medical products.” — Maximilian Alber “One needs to build a technology that either makes a difference in the long run, or one must be able to innovate at a very fast pace.” — Maximilian Alber Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model. | |||
| Subseasonal-to-Seasonal Weather Forecasting with Sam Levang from Salient Predictions | 13 mai 2024 | 00:16:51 | |
Advanced weather forecasts are the new frontier in meteorology. Long-term forecasting has garnered significant attention due to its potential to provide valuable insights to various sectors of society and the economy. In today’s episode, Sam Levang, Chief Scientist at Salient, joins me to discuss Salient’s innovative approach to weather forecasting. Salient specializes in providing highly accurate subseasonal-to-seasonal weather forecasts ranging from 2 to 52 weeks in advance. In our conversation, we discuss the ins and outs of the company’s innovative approach to weather forecasting. We delve into the hurdles of subseasonal-to-seasonal forecasting, how machine learning is replacing traditional weather modeling approaches, and the various inputs it uses. Discover the value of machine learning for post-processing of data, the type of data the company utilizes, and why it uses probabilistic models in its approach. Gain insights into how Salient is catering to the impacts of climate change in its weather predictions, the company’s approach to validation, how AI has made it all possible, and much more! Key Points:
Quotes: “Salient produces weather forecasts that extend further into the future than most people are used to seeing. We go up to a year in advance.” — Sam Levang “ML (Machine Learning) models have proved to be actually a very effective replacement for the traditional approach to weather modeling.” — Sam Levang “The only difference about making forecasts longer timescales of weeks and months ahead is that there are some differences in the particular parts of the climate system that provide the most predictability.” — Sam Levang “While ML and AI are extremely powerful tools, they are still just tools and there's so much else that goes into building a really valuable product, or a service, or a company.” — Sam Levang Links: Resources for Computer Vision Teams: LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model. | |||
| Virtual Tissue Staining with Yair Rivenson from PictorLabs | 06 mai 2024 | 00:34:11 | |
Welcome to today’s episode of Impact AI, where we dive into the groundbreaking world of virtual tissue staining with Yair Rivenson, the co-founder and CEO of PictorLabs, a digital pathology company advancing AI-powered virtual staining technology to revolutionize histopathology and accelerate clinical research to improve patient outcomes. You’ll find out how machine learning is used to translate unstained tissue autofluorescence into diagnostic-ready images, gain insight into overcoming AI hallucinations and the rigorous validation processes behind virtual staining models, and discover how PictorLabs navigates challenges like large files and bandwidth dependency while seamlessly integrating technology into clinical workflows. Yair also provides invaluable advice for AI-powered startup leaders, emphasizing the importance of automation and data quality. To gain deeper insights into the transformative potential of virtual tissue staining, tune in today! Key Points:
Quotes: “The most important factor for the healthcare system, for the patient is the fact that you can get all the results, all the workup, and all the different stains from a single tissue section very, very fast.” — Yair Rivenson “Machine learning is the engine behind virtual staining. In a sense, that’s what takes those images from the autofluorescence of the unstained tissue section and converts [them] into a stain that pathologists can use for their diagnostics.” — Yair Rivenson “At the end of the day, the network is as good as the data that it learns from.” — Yair Rivenson “The more you automate, the better off you’ll be in the long run.” — Yair Rivenson Links: ‘Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning’
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model. | |||
| Improving Recycling Efficiency with Nikola Sivacki from Greyparrot | 29 avr. 2024 | 00:20:31 | |
One of the most powerful impacts machine learning can make is helping to solve environmental challenges all around the world. Today on Impact AI, I am joined by the founder of Greyparrot, Nikola Sivacki to discuss how his company uses machine learning to improve recycling efficiency. Learn all about Nikola’s background, what Greyparrot does, their services, the importance of their work, the role machine learning plays in it, how they gather and annotate data, the challenges they face, how they develop new models, and so much more. Tune in to hear the newest AI innovations Nikola is most excited about before hearing his goals for Greyparrot in the near future. Lastly, get some valuable advice for running AI-powered startups. Key Points:
Quotes: “Greyparrot basically monitors the flow of waste materials, recyclable materials in material recovery facilities, and offers compositional analysis of these materials.” — Nikola Sivacki “It's very helpful, – if thinking of a new product, to start with a data set that is really tailored to answering the main uncertain question that is posed there.” — Nikola Sivacki “Start thinking about data from the start. I think that it’s very important to understand the data in detail.” — Nikola Sivacki “Our goal is to improve, of course, recycling rates globally so that we can reduce reliance on virgin materials.” — Nikola Sivacki Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model. | |||
| Discovering the Microbiome with Leo Grady from Jona | 22 avr. 2024 | 00:23:01 | |
What if AI could decode the mysteries of your microbiome for a healthier you? In this episode, I sit down with Leo Grady, Founder and CEO of Jona, to discuss his groundbreaking work in microbiome research. Jona is a health technology company that specializes in microbiome profiling and analysis. It offers microbiome testing kits for individuals to use at home, along with AI-powered analysis of the associated microbiome data. In our conversation, we delve into the human microbiome and how Jona is harnessing the power of AI to unlock its secrets and revolutionize healthcare practices. Discover how Jona bridges the gap between research and clinical practice and utilizes deep shotgun metagenomic sequencing. We discuss why he thinks AI is a critical technology for decoding the microbiome, how Jona is able to connect research findings to microbiome profiles, and the company’s approach to model validation. Gain insights into the evolving landscape of AI in healthcare, the number one barrier to clinical translation and adoption of AI technology, what needs to be done to overcome it, and much more. Key Points:
Quotes: “What's really remarkable to me about the microbiome is that it's been linked to almost every aspect of human health.” — Leo Grady “There are a lot of challenges that forced us to really develop new kinds of [machine learning] techniques that are really suited to this problem. We can't just rely on taking what's out there today.” — Leo Grady “The AI is doing that extraction. We have human oversight to make corrections to it. But once that paper has been extracted correctly, then we don't need to look at it again. It’s a one-time review process on every study.” — Leo Grady “I think the biggest challenges with AI and healthcare today are no longer technical, and they're no longer regulatory. The fact is that with current AI technology and enough data, we can solve almost any AI problem that we want to.” — Leo Grady Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model. | |||
| Monitoring Forests with David Marvin from Planet | 15 avr. 2024 | 00:45:12 | |
Bringing transparency and accuracy to the marketplace by producing high-quality data on all types of hard problems is a main focus for today’s guest and the company he works for. I am pleased to welcome David Marvin to Impact AI. David was the Co-Founder and CEO of Salo Sciences, which was acquired by Planet last year, and is now the Product Lead for Forest Ecosystems there! He joins me today to talk about monitoring forests. We delve into his background and path to Salo Sciences and their eventual acquisition by Planet; including the original mission and vision and what they worked to accomplish at Salo. David then explains his goals and focus at Planet, and unpacks the types of satellite imagery, models, and sensors they incorporate into their data and outputs. He highlights their approach to validation, how they are reducing bias, and how they are integrating extensive knowledge to empower their machine learning developers to create powerful models. Key Points:
Quotes: “A company like Planet was essentially probably the only company we would have really ever been acquired by just given their vision and the fact that they have their own satellites and we’re a satellite software company.” — David Marvin “[At Salo Sciences] we leveraged high-quality airborne LiDAR measurements of forests all over California. Airborne LiDAR is one of these technologies, these sensors, that was on that airplane back in my post-doc lab. It shoots out hundreds of thousands of pulses of laser light per second and reflects back to the sensor, and it can basically recreate in three dimensions a forest, or a city, whatever your mapping target is. It's extremely precise. It's centimeter-level accuracy, and it's very high-quality data. We consider that the gold standard of forest measurement.” — David Marvin
“We approach the validation aspect from a few different angles, trying to source as many different independent data sets as possible to do validation. Then we also like to do comparisons to well-known public data sets; either from academia or from governments.” — David Marvin
“Making sure you have scientists on your team, making sure you're still active in the scientific publishing community, that you're up on the latest papers that are coming out, and basically acting like a scientist in an industry position is crucial to make any product work; especially in branding markets, like forest monitoring and carbon markets.” — David Marvin Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model. | |||
| Foundation Models for Pathology with Razik Yousfi from Paige | 08 avr. 2024 | 00:24:12 | |
Foundation models have been at the forefront of AI discussions for a while now and joining me today on Impact AI is a leader in the creation of foundation models for pathology, Senior Vice President of Technology at Paige AI, Razik Yousfi. Tuning in, you’ll hear all about Razik’s incredible background leading him to Paige, what the company does and how it’s revolutionizing cancer care, and the role machine learning plays in pathology. Razik goes on to explain what foundation models are, why they are so helpful, how to train one, the differences in training one for pathology specifically, and how they use foundation models at Paige AI. We then delve into the challenges associated with the creation of foundation models before my guest shares some advice for leaders in machine learning. Finally, Razik tells us where he sees Paige AI in the next few years. Key Points:
Quotes: “Paige is focusing on digital and computational pathology. In other words, we really bring AI and novel AI solutions to the field of pathology to help pathologists make better-informed decisions.” — Razik Yousfi “A foundational model is a model trained on a very large set of data. The idea there is that you can, in turn, use that foundation model to build a wide range of downstream applications.” — Razik Yousfi “Building a foundation model is not easy. So, I wouldn't necessarily recommend to every organization to build a foundation model.” — Razik Yousfi Links:
LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model. | |||